A cardiac control or monitoring device processes cardiac electrical signals for numerous purposes. For example, a pacemaker may analyze one type of cardiac electrical signal, an intracardiac electrogram (IEGM), for controlling its mode of operation. Implantable cardiac devices may telemeter intracardiac electrograms for diagnostic purposes. Such devices may also store data representative of intracardiac electrogram signals in a memory within the implanted device for subsequent analysis and/or for later transmission via telemetry. Likewise, an external cardiac monitoring device may store data representing cardiac electrical signals after sensing of such signals from sensors affixed to the surface of the body or after receiving telemetered cardiac signal data which is transmitted by an implanted device. One problem with the storage of cardiac electrical signal data in either implanted or external devices is that the memory capacity of such devices is limited.
It is a goal of the present invention to provide for the storage of cardiac electrical signal data in a compressed form, thereby reducing the memory requirements of a cardiac control or monitoring device.
One method of reducing data storage requirements in a cardiac pacemaker which senses cardiac electrical signals is disclosed by Langer et al. in U.S. Pat. No. 4,567,883, entitled "Data Compression of ECG Data Using Delta Modulation", issued Feb. 4, 1986. The Langer et al. invention provides for more efficient utilization of storage memory by performing delta modulation data compression on sensed electrocardiogram data. More specifically, this delta modulation technique is termed a continuously variable slope delta modulator technique which was previously employed in voice transmission technology. This method reduces memory storage requirements by a factor of three or four (at a data rate of 200 bits per second).
Although this reduction in storage requirements is helpful, the massive volume of cardiac signal data for long-term recording which is necessary for detecting changes in cardiac function makes further reductions desirable.
Further reductions in data storage requirements are possible using the technique disclosed by Hansen et al. in U.S. Pat. No. 4,716,903, entitled "Storage in a Pacemaker Memory", issued Jan. 5, 1988. Instead of storing a representation of single amplitude or amplitude changes in regular time increment, this technique involves the storage of a time interval which has elapsed in which an input changes by a threshold amount. Further improvements are made possible by the present invention.
A knowledge of the nature of cardiac electrical signals is fundamental to an understanding of the present invention. Cardiac electrical signals are highly periodic in nature. Even patients with diseased hearts will display cardiac signal waveforms which are virtually identical for a vast majority of cardiac cycles. Furthermore, when the heart is functioning in an abnormal state in which the cardiac electrical signal waveform is different from a normal state, the abnormal waveforms will often take a second form, or other standard abnormal form, which is essentially identical from cycle to cycle. Accordingly, it is not necessary to store each sample of the cardiac electrical signal waveform to restore all the information contained in the signal. If an apparatus can identify and classify a signal for a particular cardiac cycle, it can completely restore a cardiac signal by storing only representative copies of the waveform associated with particular exemplary cardiac rhythms, together with information concerning the rate and order in which the representative waveforms occur.
One technique for identifying and classifying cardiac signal waveforms is to correlate an input signal with a standard waveform representative of a known cardiac rhythm. Correlation is the summation of the products of point-by-point multiplications of two sequences for the purpose of deriving a standard of similarity between the two waveforms. Unfortunately, correlation analysis requires such computational complexity that it is impractical in an implanted device. Because the device expends energy on each computational step and correlation requires so many computations, the lifetime of an implanted device performing correlation would be unreasonably short or the battery size too large for practical usage.
It is known that correlation analysis of intracavitary ventricular electrograms is a viable technique for analyzing cardiac waveform morphology, and that it improves specificity of cardiac rhythm recognition. For example, correlation waveform analysis is a reliable technique for discriminating ventricular tachycardia from sinus rhythm. It has been used for over two decades in the analysis of surface lead morphology as well as for analyzing esophageal electrograms, intra-ventricular electrograms and intra-atrial electrograms. While correlation analysis is effective, it requires a waveform sampling rate of about 1 kHz to properly distinguish arrhythmia waveforms. Furthermore, the number of computations it requires is too demanding for usage in the low energy environment of an implantable device.
One modified technique for performing standard correlation is by multiplying the waveform sequences in a section-by-section manner, called piecewise correlation analysis, which provides for a reduction in the number of required computations by limiting the correlation procedure to operate only in the vicinity of the R-wave. In one example of piecewise correlation, a signal processing system defines a representative "normal" signal by measuring a ventricular electrogram signal template when the heart is functioning with a normal sinus rhythm. The system specifies this template by "windowing" the waveform, detecting the QRS complex of the cardiac signal and storing a predetermined number of samples before and after the QRS complex. For example, a waveform window may include 64 samples, which contain the QRS complex and are acquired at a 1000 Hz rate. The system averages a number of these waveform windows for a preset number of cardiac cycles with the QRS complex for each cardiac cycle occurring at the same sample location within the window. After sampling and storing the template waveform, the system samples the ventricular electrogram at the same rate and for the same number of samples as was done when acquiring the template samples. The system correlates these samples with the average sinus rhythm template on a beat-by-beat basis.
To provide accurate analysis of cardiac rhythms, the piecewise correlation technique requires that the QRS complexes of the template and the sample electrogram are aligned. In piecewise correlation analysis, accurate template alignment is very important to successfully distinguish various cardiac rhythms, such as ventricular tachycardia from normal sinus rhythm or atrial fibrillation. In practice, alignment errors greater than four to five milliseconds cause a large and unpredictable variability in correlator results. Furthermore, alignment errors frequently are not recognized since a sensing determination aligned on some feature other than the R-wave may still result in a high correlation output.
Reliable template alignment is not a simple procedure. For example, a system which aligns R-waves according to a measured point of maximum intracardiac electrogram (IEGM) amplitude or corresponding to the peak derivative of the signal does not provide adequate alignment due to the large variability in amplitude and slope of the signal waveform. Signal processing of the cardiac signal to clarify the position of the R-wave using a variety of search windows and filtering techniques is helpful for particular signal morphologies but no single alignment procedure is adequate for all patients. The wide variability in cardiac signal morphologies for different patients and also for different times for the same patient cause these alignment difficulties.
Furthermore, a system which performs window alignment based on the peak cardiac signal amplitude is susceptible to errors from T-wave sensing. Occasional patients may display T-waves which are consistently larger in amplitude than R-waves. Consequently, windows may align on the T-wave or may align on the R- and T-waves in alternating cardiac cycles. Systems which align the template and sample signals based on the location of the sensed peak derivative commonly err from five to ten milliseconds because of the noisy nature of derivative signals. When combined with low pass filtering, alignment by peak derivative sensing improves somewhat but remains unacceptable.
The small size of the piecewise correlation window which is necessary to provide the computational efficiency for an implantable device leads to an additional source of alignment error. As the device performs piecewise correlation over a single cardiac cycle it may detect multiple peaks, possibly caused by T-wave sensing or detection of multiple peaks associated with the R-wave.
Full scanning correlation, in which a continuously sampled cardiac signal is correlated with a template sequence having a predetermined length smaller than the duration of the shortest possible cardiac cycle, avoids the alignment problems inherent in piecewise correlation. Unfortunately, full scanning correlation requires an excessive number of computations, and therefore too much power drain, for an implantable device.
It is, therefore, a primary object of the present invention to reduce the data storage and transmission requirements of a diagnostic test device.
It is a further object of the present invention to reduce computational and energy requirements of the a diagnostic test device by compressing cardiac electrical signal data prior to processing and storing the data.
Further objects and advantages of this invention will become apparent as the following description proceeds.